Back in the 17th century, John Dryden wrote, “He who would search for pearls must dive below.” Although the author did not have advanced data analytics in mind, the quote perfectly describes its essence. Let’s find out how deep one should go into data in search of a much-needed and fact-based insight.
Types of data analytics
There are 4 different types of analytics. Here, we start with the simplest one and go further to more the sophisticated. As it happens, the more complex an analysis is, the more value it brings.
Descriptive analytics answers the question of what happened. For instance, a healthcare provider will learn how many patients were hospitalized last month; a retailer – the average weekly sales volume; a manufacturer – a rate of the products returned for a past month, etc. Let us also bring an example from our practice: a manufacturer was able to decide on focus product categories based on the analysis of revenue, monthly revenue per product group, income by product group, total quality of metal parts produced per month.
Descriptive analytics juggles raw data from multiple data sources to give valuable insights into the past. However, these findings simply signal that something is wrong or right, without explaining why. For this reason, highly data-driven companies do not content themselves with descriptive analytics only and prefer combining it with other types of data analytics.
At this stage, historical data can be measured against other data to answer the question of why something happened. Thanks to diagnostic analytics, there is a possibility to drill down, find out dependencies and identify patterns. Companies go for diagnostic analytics as it gives in-depth insights into a particular problem. At the same time, a company should have detailed information at their disposal otherwise data collection may turn out to be individual for every issue and time-consuming.
Our BI demo shows how a retailer can drill the sales and gross profit down to categories to find out why they missed their net profit target. Another flashback to our BI projects: in the healthcare industry, customer segmentation coupled with several filters applied (like diagnoses and prescribed medications) allowed measuring the risk of hospitalization.
Predictive analytics tells what is likely to happen. It uses the findings of descriptive and diagnostic analytics to detect tendencies, clusters and exceptions, and to predict future trends, which makes it a valuable tool for forecasting. Despite numerous advantages that predictive analytics brings, it is essential to understand that forecasting is just an estimate, the accuracy of which highly depends on data quality and stability of the situation, so it requires careful treatment and continuous optimization.
Thanks to predictive analytics and the proactive approach it enables, a telecom company, for instance, can identify the subscribers who are most likely to reduce their spend and trigger targeted marketing activities to remediate; a management team can weigh the risks of investing in their company’s expansion based on cash flow analysis and forecasting. One of our case studies describes how advanced data analytics allowed a leading FMCG company to predict what they could expect after changing brand positioning.
The purpose of prescriptive analytics is to literally prescribe what action to take to eliminate a future problem or take full advantage of a promising trend. An example of prescriptive analytics from our project portfolio: a multinational company was able to identify opportunities for repeat purchases based on customer analytics and sales history.
This state-of-the-art type of data analytics requires not only historical data but also external information due to the nature of statistical algorithms. Besides, prescriptive analytics uses advanced tools and technologies, like machine learning, business rules and algorithms, which makes it sophisticated to implement and manage. That is why, before deciding to adopt prescriptive analytics, a company should compare required efforts vs. an expected added value.
To identify if there is a prevailing type of data analytics, let’s turn to different surveys on the topic for the period 2016-2019.
For 2016 Global Data and Analytics Survey: Big Decisions, PwC asked more than 2,000 executives to choose a category that described their company’s decision-making process best. Further, C-suite was questioned with what type of analytics they relied on most. The results were the following: descriptive analytics dominated (58%) in the “Rarely data-driven decision-making” category; diagnostic analytics topped the list (34%) in the “Somewhat data-driven” category; predictive analytics (36%) led in the “Highly data-driven” category.
That survey showed a need for one or the other type of analytics at different stages of a company’s development. In fact, the companies that strived for informed decision-making found descriptive analytics insufficient and added up diagnostics analytics or even went as far as predictive one.
For another survey, BARC’s BI Trend Monitor 2017, 2,800 executives shared their opinion on the growing importance of advanced and predictive analytics. The term advanced analytics was the umbrella term for predictive and prescriptive analytics types.
According to 2018 Advanced and Predictive Analytics Market Research, advanced and predictive analytics were for the first time considered “critical” or “very important” by a majority of respondents.
Within the BARC's BI Trend Monitor 2019 survey, C-suite still named advanced analytics among the most important business intelligence trends.
To sum up
With various types of analytics, companies are free to choose how deep they need to dive in data analysis to satisfy their business needs best. While descriptive and diagnostic analytics offer a reactive approach, predictive and prescriptive analytics make users proactive. Meanwhile, current trends show that more and more companies come to the situation when they need advanced data analysis and choose to adopt it.
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